Big Data simply refers to a large volume of the varied amount of data. Here the varied amount of data means that the data may be structured or unstructured. This data is extremely important to organizations as it can be used to get valuable insights and help them to take better strategic decisions. There are a number of topics in big data for thesis and research but before that let us discuss the basics of big data.
Three Vs of Big Data
Big Data can be described in terms of three Vs which are:
Volume – It defines the amount of data produced from different sources such as social media, business transactions.
Velocity – It refers to the rate at which the data is produced.
Variety – It refers to different formats of data produced which may be structured, semi-structured, or unstructured.
Value and variety are the two new Vs that have evolved in big data in the recent times.
Since the amount of data produced is huge, an open-source distributed processing framework is designed to manage that data which is known as Hadoop. This framework helps in processing, storing, and managing the big data. It was developed under Apache Software Foundation and was thus known as Apache Hadoop. The Big Data Hadoop architecture consists of the following components:
- Hadoop Common – Java libraries and utilities
- Hadoop Yarn – For job scheduling
- Hadoop Distributed File System – For high throughput
- Hadoop MapReduce – For parallel processing
Importance of Big Data
Big Data technology has emerged a lot in the past few years. Many companies are adopting the big data technologies to study the data patterns which will help in the growth of their business by identifying new opportunities. Big Data technologies like Hadoop and cloud-based analytics provide cost-based advantages to the companies by finding efficient ways to store data. With big data analytics, businesses can make faster and better decisions. Moreover, it also helps in getting valuable customer insights regarding what they want and what are their needs. All these things make big data important for businesses.
Applications of Big Data
Big Data finds its applications in different areas such as retail, finance, health, media, telecommunication, e-commerce to develop better marketing strategies. Following are the main applications of big data:
- Campaign management
- Supply-chain management
- Trade surveillance
- Fraud detection
- Chain management
- Event analytics
- Power investigation
- Ecological fortification
- Identifying shopping patterns
- Traffic management
Thesis and Research Areas in Big Data
Following is the list of trending areas to find good topics in big data for thesis and research:
- Big Data Hadoop
- Big Data Tools
- Big Data with R
- Big Data Security
- Big Data Analytics
- Big Data and Cloud Computing
Big Data Hadoop
The concept of Hadoop has already been discussed earlier that it is an open-source framework for processing and managing big data. It is a good area to look for good thesis topics in big data. A single Hadoop cluster has a single master node and multiple slave nodes. Hadoop File System(HDFS) is used for distributed processing and storage of data. It also provides high aggregate bandwidth across the cluster. The importance of the Hadoop lies in the fact that more than half of the data produced is unstructured. This technology is required to optimize that data properly. Following are the advantages of Hadoop:
- Can process and store huge amount of data
- High computing power
- Fault tolerance ability
- Low-cost framework
- Flexibility and scalability
MapReduce is an algorithm for processing huge amount of data. This algorithm has two tasks to perform – Map and Reduce. In Map task, a set of data is taken and converted into another form by breaking down it into tuples. In the Reduce task, the output of the Map i.e. tuples is taken and combined together to form small tuples. The Reduce job follows the Map job. It is also a good field for research and thesis under Big Data. Two primitives mappers and reducers are used in the MapReduce model. The mapper processes the input data while the reducer process the data from the mapper. The MapReduce Model has the following main components:
- JobHistory Server
Big Data Tools
Tools are used to analyze and process the data. Like Apache Hadoop, there are various other big data tools to manage data which is so large that it even exceeds terabytes in size. The big data tools are categorized on the basis of storage and processing. You can pick up a tool and start your research in that. Following are the top tools used in Big Data Analytics:
- Apache Hadoop
- Apache Storm
- Apache Samoa
Big Data Analytics is expected to see a fourfold increase in the near future and these tools are going to help the companies for processing and analyzing the data. It depends upon the company regarding which tool to use for their business.
Big Data with R
It is one of the hot topics in Big Data for thesis and research. R is a programming language which acts as an interface to software developed in languages like C, C++, and FORTRAN. It is the language for data exploration and development. R and Hadoop can be integrated together in the following ways:
- Hadoop Streaming
To manage big data with R following strategies are utilized:
- Sampling – This strategy is used to reduce the data which is too big to handle
- Use of bigger hardware – Bigger hardware helps in cases when the data gets large
- Storing objects on hard disc – Instead of storing data in memory, it can be stored on hard disc and can be analyzed set wise.
- Integrating languages like C++ and Java – It is another good strategy to increase the efficiency.
Big Data Security
No doubt big data is one of the emerging technologies, there are also security concerns of this technology. Big Data security is a term used to represent all the measures, practices, and tools used to protect the data from malicious attacks or thefts. It is an interesting area for research and thesis in big data. The threat can be either online or offline. To protect the data it is best to implement big data security. The main security concerns in big data include corrupting of the incoming data, threat of stored data being stolen, third part attack. Encryption technique is used to protect the data from these security problems. Firewall is another useful big data security tool that can filter the traffic.
Big Data Analytics
Big Data Analytics is a hot research area under big data. It is a strategy to analyze large volumes of data collected from a number of resources such as social networks, digital media, transactions etc. The main aim of data analytics is to identify different data patterns from large datasets. There are different data analytics tools used for this purpose. Data analytics helps in getting useful insights for the business and will help in making better business decisions. Students can work on a Ph.D. thesis on big data analytics. It is being used in travel and hospitality business, healthcare, government sector, and retail. Following are the major benefits of big data analytics:
- Big data analytics help companies to achieve financial efficiency.
- It speeds up the decision-making process.
- Provides new business opportunities to the companies.
- Data can be visualized in a better way using charts, graphs, and slide decks.
Big Data and Cloud Computing
Big data and cloud computing are considered an ideal combination and this combination is also a good area for research and thesis. Big Data in cloud computing helps organizations to process, analyze, and manage the large datasets more efficiently. This combination of big data and cloud computing provides a scalable solution to big data and business analytics. Apart from this, it provides the following advantages:
Cloud Computing provides different resources for storing and managing data. With cloud database, a company can have a number of virtual servers.
The services are affordable as the companies have to pay for storage space and the power usage.
With big data tools like Apache Hadoop coupled with cloud computing, both structured and unstructured data can be processed easily.
To increase the processing power, there is no need to add more physical servers as with cloud services, a company can have a number of virtual servers.
It is also one of the good topics in big data for thesis and research. It is an analytical application that combines the SQL-style querying with the Hadoop framework. SQL queries are used for computing clusters. Different types of SQL-on-Hadoop engines have been introduced. An optimized data format is used to improve the performance of SQL-on-Hadoop. There are various SQL-on-Hadoop tools and following are some of these tools:
- Apache Hive
- Cloudera Impala
Latest Thesis and Research Topics in Big Data
Here is the list of latest topics in big data for thesis and research:
- Privacy-preserving big data publishing: a scalable k-anonymization approach using MapReduce.
- Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark.
- Efficient and Rapid Machine Learning Algorithms for Big Data and Dynamic Varying Systems
- Disease Prediction by Machine Learning Over Big Data From Healthcare Communities
- A Parallel Multi-classification Algorithm for Big Data Using an Extreme Learning Machine
These are some of the good topics in big data for M.Tech thesis and dissertation. You can also work on the Ph.D. thesis on big data analytics. There are various Ph.D. topics in big data analytics.
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